{
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   "metadata": {
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     "end_time": "2024-09-19T12:04:33.976144Z",
     "start_time": "2024-09-19T12:04:33.971629Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import gc\n",
    "import warnings\n",
    "from pylab import mpl\n",
    "\n",
    "# 设置显示中文字体\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# 设置正常显示符号\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "warnings.filterwarnings('ignore')"
   ],
   "id": "a4a65699adfbc3a7",
   "outputs": [],
   "execution_count": 339
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:54.149279Z",
     "start_time": "2024-09-19T12:04:34.874619Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_data = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\test_format1.csv')\n",
    "train_data = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\train_format1.csv')\n",
    "user_info = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\user_info_format1.csv')\n",
    "user_log = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\user_log_format1.csv')"
   ],
   "id": "391783632ec9d0d6",
   "outputs": [],
   "execution_count": 340
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:54.157799Z",
     "start_time": "2024-09-19T12:04:54.151284Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# reduce memory usage\n",
    "def reduce_mem_usage(df, verbose=True):\n",
    "    start_mem = df.memory_usage().sum() / 1024 ** 2\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "\n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        if col_type in numerics:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)\n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "    end_mem = df.memory_usage().sum() / 1024 ** 2\n",
    "    if verbose: print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    return df"
   ],
   "id": "32548f523fc3dc3f",
   "outputs": [],
   "execution_count": 341
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:55.737611Z",
     "start_time": "2024-09-19T12:04:54.158805Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_data = reduce_mem_usage(test_data)\n",
    "train_data = reduce_mem_usage(train_data)\n",
    "user_info = reduce_mem_usage(user_info)\n",
    "user_log = reduce_mem_usage(user_log)"
   ],
   "id": "380f720fadfac293",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 5.98 MB\n",
      "Memory usage after optimization is: 3.49 MB\n",
      "Decreased by 41.7%\n",
      "Memory usage of dataframe is 5.97 MB\n",
      "Memory usage after optimization is: 1.74 MB\n",
      "Decreased by 70.8%\n",
      "Memory usage of dataframe is 9.71 MB\n",
      "Memory usage after optimization is: 3.24 MB\n",
      "Decreased by 66.7%\n",
      "Memory usage of dataframe is 2933.33 MB\n",
      "Memory usage after optimization is: 890.48 MB\n",
      "Decreased by 69.6%\n"
     ]
    }
   ],
   "execution_count": 342
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:55.745161Z",
     "start_time": "2024-09-19T12:04:55.738617Z"
    }
   },
   "cell_type": "code",
   "source": "train_data.head()",
   "id": "1ea5a0aafd1470a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label\n",
       "0    34176         3906      0\n",
       "1    34176          121      0\n",
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   "cell_type": "code",
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    {
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       "   user_id  merchant_id  prob\n",
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       "1   360576         1581   NaN\n",
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   "cell_type": "code",
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   "cell_type": "code",
   "source": "user_log.head()",
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    {
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      "text/plain": [
       "   user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type\n",
       "0   328862   323294     833       2882    2660.0         829            0\n",
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     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 346
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:55.782349Z",
     "start_time": "2024-09-19T12:04:55.775860Z"
    }
   },
   "cell_type": "code",
   "source": "train_data.isna().sum()",
   "id": "2e6b5f5b1a35b86d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0\n",
       "merchant_id    0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 347
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:55.792072Z",
     "start_time": "2024-09-19T12:04:55.783354Z"
    }
   },
   "cell_type": "code",
   "source": "user_info.isna().sum() / user_info.shape[0]",
   "id": "12b96df95748c240",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      0.000000\n",
       "age_range    0.005227\n",
       "gender       0.015173\n",
       "dtype: float64"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 348
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:56.277967Z",
     "start_time": "2024-09-19T12:04:55.793085Z"
    }
   },
   "cell_type": "code",
   "source": "user_log.isna().sum() / user_log.shape[0]",
   "id": "a7e2a26d104ee7af",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id        0.000000\n",
       "item_id        0.000000\n",
       "cat_id         0.000000\n",
       "seller_id      0.000000\n",
       "brand_id       0.001657\n",
       "time_stamp     0.000000\n",
       "action_type    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 349
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:56.721817Z",
     "start_time": "2024-09-19T12:04:56.279597Z"
    }
   },
   "cell_type": "code",
   "source": [
    "label_gp = train_data.groupby('label')['user_id'].count()\n",
    "print('正负样本数据量 : \\n', label_gp)\n",
    "_, axe = plt.subplots(1, 2, figsize=(12, 6))\n",
    "train_data.label.value_counts().plot(kind='pie', autopct='%1.1f%%', shadow=True, explode=[0, 0.1], ax=axe[0])\n",
    "sns.countplot(x='label', data=train_data, ax=axe[1])"
   ],
   "id": "744404ebadc08d2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正负样本数据量 : \n",
      " label\n",
      "0    244912\n",
      "1     15952\n",
      "Name: user_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 350
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "c268f4de171ef8d3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:57.022950Z",
     "start_time": "2024-09-19T12:04:56.722823Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print('选取图片top5店铺\\n店铺\\t购买次数')\n",
    "print(train_data.merchant_id.value_counts().head(5))\n",
    "train_data_merchant = train_data.copy()\n",
    "train_data_merchant['Top5'] = train_data_merchant['merchant_id'].apply(\n",
    "    lambda x: 1 if x in [4044, 3828, 4173, 1102, 4976] else 0)\n",
    "train_data_merchant = train_data_merchant[train_data_merchant['Top5'] == 1]\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.title('Merchant VS Label')\n",
    "ax = sns.countplot(x='merchant_id', hue='label', data=train_data_merchant)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()"
   ],
   "id": "be2771c4550d3560",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "选取图片top5店铺\n",
      "店铺\t购买次数\n",
      "merchant_id\n",
      "4044    3379\n",
      "3828    3254\n",
      "4173    2542\n",
      "1102    2483\n",
      "4976    1925\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 351
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:57.648086Z",
     "start_time": "2024-09-19T12:04:57.023955Z"
    }
   },
   "cell_type": "code",
   "source": [
    "user_repeat_buy = [rate for rate in train_data.groupby(['user_id'])['label'].mean() if rate <= 1 and rate > 0]\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "#直方图\n",
    "sns.distplot(user_repeat_buy, fit=stats.norm)\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "#qq图\n",
    "res = stats.probplot(user_repeat_buy, plot=plt)"
   ],
   "id": "650a64aa48b5f4e0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 352
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:58.845691Z",
     "start_time": "2024-09-19T12:04:57.651742Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "train_data_user_info = train_data.merge(user_info, on='user_id', how='left')\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.title('Gender VS Label')\n",
    "#   确定索引类型是否出错\n",
    "# print(train_data_user_info.index.dtype)\n",
    "# 将数据框转换为字典再转换回数据框\n",
    "temp_dict = train_data_user_info.to_dict()\n",
    "train_data_user_info = pd.DataFrame(temp_dict)\n",
    "\n",
    "ax = sns.countplot(x='gender', hue='label', data=train_data_user_info)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n"
   ],
   "id": "539099b6158ed7d6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 353
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:59.212043Z",
     "start_time": "2024-09-19T12:04:58.847212Z"
    }
   },
   "cell_type": "code",
   "source": [
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['gender'])['label'].mean()]\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "res = stats.probplot(repeat_buy, plot=plt)"
   ],
   "id": "ce300d675ffa1bcf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 354
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:04:59.813466Z",
     "start_time": "2024-09-19T12:04:59.213050Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.title('Age VS Label')\n",
    "ax = sns.countplot(x='age_range', hue='label', data=train_data_user_info)"
   ],
   "id": "8ae41403672578a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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ll17Uu+/+QTabXbNmza6w+RG9AAAAhnG5LOXm/iC7vWL+utlPHbO0f4I4Lq6zFi5cpvT0NLVu3UZBQXUrZnIiegEAAIxUlgj1hvr1Gyg2Nq7Cj8Mb2QAAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDw+vQEAAMBAdrut0n9kmScRvQAAAIax220KrhsgewX9dbNrcRUWKif3XKUNX6IXAADAMHa7TXaHQ6eW/k4XT2VU+PF8G/xMDR6ZJrvdVqrozc3N1VNPDdXMmX/SrbfeVoEzJHoBAACMdfFUhi4e3+vtaVxTbm6unnvuWR079r1Hjscb2QAAAOBxU6a8oO7d7/fY8YheAAAAeNzzz7+oAQMe99jxiF4AAAB43G233e7R4xG9AAAAMB7RCwAAAOMRvQAAADAeH1kGAABgKN8GPzPqODeD6AUAADCMy2XJVVioBo9M89wxCwsr7V9jk4heAAAA47hclnJyz8lut3n0mGWJ3g0btlbAbIojegEAAAxU1gg1FW9kAwAAgPGIXgAAABiP6AUAADCAZZl7KkN53DeiFwAAoApzOBySpIKCC16eScW5ct8cjrK/HY03sgEAAFRhdrtDAQGBys/PkST5+fnLZvPcpzZUJMuyVFBwQfn5OQoICJTdXvbXa4leAACAKq5OnXqS5A5f0wQEBLrvY1kRvQAAAFWczWZTUFB91a4drMLCS96eTrlyOHxu6hXeK4heAAAAQ9jtdtntft6eRqXEG9kAAABgPKIXAAAAxvNq9M6YMUMjRoxwf71v3z71799fUVFRSkpKKvKZbJs3b1avXr0UExOjuXPnFrmd1atXq2vXroqLi9PKlSuLjM2bN0+xsbFKSEjQxo0bK/YOAQAAoFLyWvSmpaVp/vz5evHFFyVJBQUFGjFihFq3bq0lS5bo4MGDWrp0qSQpOztbI0eOVO/evbVw4UKlpKRo06ZNki6H8vjx4zVq1Ch98MEHmjlzpjIyMiRJ69evV1JSkqZOnaoZM2Zo4sSJyskx812NAAAA+GleiV6Xy6XJkyfriSeeUEhIiCRp3bp1ys/P14QJE9S0aVONHTtWixcvliStWLFCjRo10ujRo9W8eXONGjXKPbZo0SLFxMRowIABCgsL0+DBg7V8+XJJ0oIFC9SvXz9169ZN7du3V0JCgtauXeuNuwwAAAAv8kr0LliwQPv27dPtt9+uL7/8UgUFBUpLS1NERIQCAgIkSWFhYTp48KAkKT09XTExMe4PWg4PD9fu3bslXX7FuEOHDu7bvtHYrl27Sj1fm40LFy7lcfEmb993Lly4cOFScZeS8PhHljmdTs2aNUshISH6/vvvtXz5cr333nuKjIxUkyZN3NvZbDbZ7Xbl5eUpPz9fLVu2dI8FBgYqKyvLfXtX71fSsdKoX792qfcBUHkEB9fy9hQAAF7m8ehds2aNzp07p48++kj16tXTpUuX1KdPHy1ZskSPPPJIkW39/f11/vx5ORwO+fn5FbteUpnHSuP06bO66j11AMrI4bB7JUBzcpwqLHR5/LgAgIpns5XsBUqPR+/x48cVERGhevUu/yk5Hx8fhYWFKSMjQ9nZ2UW2dTqd8vX1VVBQUJGxK9dLuuHY1W9cu3qsNCxLRC9QxfEcBoDqzePn9N5yyy26cOFCkeu+//57vfDCC9qxY4f7uszMTBUUFCgoKEht27YtMrZnzx41btxYkm44tn379muOAQAAoPrwePR27txZBw4c0IIFC3T8+HF9/PHHSktLU48ePZSfn68lS5ZIkmbPnq3Y2Fg5HA7Fx8dr27ZtSk1N1cWLFzVnzhzFxcVJknr27KlVq1YpPT1dTqdTycnJRcbmz5+vEydO6NSpU1q8eLF7DAAAANWHx09vCA4O1vvvv6/p06dr2rRpatiwod5++23deuutSkxM1Lhx4zR9+nTZ7XYlJydLkurVq6cJEyZo+PDhqlmzpmrXrq1p06ZJklq1aqWhQ4eqf//+8vf3V7NmzTRo0CBJUnx8vFavXq0ePXpIkjp27Oj+NwAAAKoPm2VVrjPdTp48qd27dysiIkLBwcFFxjIzM5WRkaHIyEjVqlX0zTAHDhzQiRMnFBUVVeTNa5K0c+dOnTt3TtHR0bKV9HMtrnLqFG9kA8qDj8/lN7INfnul0o5m33iHm9Tq9nqa9+yDyslx6tIl3sgGACay2aQGDSrhG9lupGHDhurSpcs1x0JCQtx/zOLHQkNDFRoaes2x8PDw8poeAAAAqiCv/RliAAAAwFOIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYz8fbE6jK7Hab7HabR47lcllyuSyPHAsAAMA0RG8Z2e021a1bUw6HZ14sLyx0KTf3B8IXAACgDIjeMrLbbXI47Jo4f70OZeVV6LFaNApS4qD7ZLfbiF4AAIAyIHpv0qGsPKUdzfb2NAAAAHAdvJENAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8r0RvYmKiwsLC3Jfu3btLkvbt26f+/fsrKipKSUlJsizLvc/mzZvVq1cvxcTEaO7cuUVub/Xq1eratavi4uK0cuXKImPz5s1TbGysEhIStHHjxoq/cwAAAKh0vBK9u3bt0vvvv68tW7Zoy5Yt+uyzz1RQUKARI0aodevWWrJkiQ4ePKilS5dKkrKzszVy5Ej17t1bCxcuVEpKijZt2iTpciiPHz9eo0aN0gcffKCZM2cqIyNDkrR+/XolJSVp6tSpmjFjhiZOnKicnBxv3GUAAAB4kY+nD3jp0iXt379fkZGRqlWrlvv6tWvXKj8/XxMmTFBAQIDGjh2rl19+Wf3799eKFSvUqFEjjR49WjabTaNGjdLixYvVoUMHLVq0SDExMRowYIAkafDgwVq+fLnGjBmjBQsWqF+/furWrZskKSEhQWvXrnVvW1I2W/nd/5tVmeYCVCU8dwDATCX9/u7x6N23b59cLpf69eunEydOKCoqSq+88orS0tIUERGhgIAASVJYWJgOHjwoSUpPT1dMTIxs/3uvwsPD9cYbb0iS0tLS9POf/9x9++Hh4XrnnXfcY+PHjy8ytmXLllJHb/36tct+h8tRcHCtG28EoBieOwAAj0fvgQMH1KJFC02aNEnBwcF67bXXNGnSJN1xxx1q0qSJezubzSa73a68vDzl5+erZcuW7rHAwEBlZWVJkpxOZ5H9SjpWGqdPn9VVpxdLkhwOu8d/kObkOFVY6PLoMYHy5I3njcRzBwBMZrOV7AVKj0dv37591bdvX/fXU6ZMUUJCglq2bCk/P78i2/r7++v8+fNyOBxFxq5cL6nMY6VhWSoWvd5SWeYBVDU8dwCgevP6R5bVr19fLpdLDRo0UHZ2dpExp9MpX19fBQUFFRm7cr2kG45d/ca1q8cAAABQfXg8epOSkpSSkuL+evv27bLb7QoLC9OOHTvc12dmZqqgoEBBQUFq27ZtkbE9e/aocePGknTDse3bt19zDAAAANWHx6O3VatWevvtt7Vx40Zt2LBBU6ZMUb9+/dSpUyfl5+dryZIlkqTZs2crNjZWDodD8fHx2rZtm1JTU3Xx4kXNmTNHcXFxkqSePXtq1apVSk9Pl9PpVHJycpGx+fPn68SJEzp16pQWL17sHgMAAED14fFzeh966CEdOHBAv/nNb+RwONSnTx+NHTtWPj4+SkxM1Lhx4zR9+nTZ7XYlJydLkurVq6cJEyZo+PDhqlmzpmrXrq1p06ZJuhzRQ4cOVf/+/eXv769mzZpp0KBBkqT4+HitXr1aPXr0kCR17NjR/W8AAABUHzbLqlxv7zh58qR2796tiIgIBQcHFxnLzMxURkZGsc/4lS5/KsSVj0D78Rvidu7cqXPnzik6Otr9sWelcepU8U9v8PG5/C70wW+vVNrR7GvvWE5a3V5P8559UDk5Tl26xDvQUXV58nkj8dwBgOrAZpMaNKiEn95wIw0bNlSXLl2uORYSEqKQkJBrjoWGhio0NPSaY+Hh4eU1PQAAAFRBXv/0BgAAAKCiEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMF6l+zPEAFDd2O022e02jx3P5bLkclkeOx4AVAZELwB4kd1uU926NeVweO4Xb4WFLuXm/kD4AqhWiF4A8CK73SaHw66J89frUFZehR+vRaMgJQ66T3a7jegFUK0QvQBQCRzKylPa0WxvTwMAjMUb2QAAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPK9H75NPPqmlS5dKkjZv3qxevXopJiZGc+fOLbLd6tWr1bVrV8XFxWnlypVFxubNm6fY2FglJCRo48aNRcbeeustRUVFqU+fPkpLS6vYOwMAAIBKyavRu2LFCm3YsEGSlJ2drZEjR6p3795auHChUlJStGnTJknSvn37NH78eI0aNUoffPCBZs6cqYyMDEnS+vXrlZSUpKlTp2rGjBmaOHGicnJyJEmffPKJPvnkE7333nt69tlnNXbsWBUUFHjnzgIAAMBrvBa9ubm5SkpKUosWLSRdDuBGjRpp9OjRat68uUaNGqXFixdLkhYtWqSYmBgNGDBAYWFhGjx4sJYvXy5JWrBggfr166du3bqpffv2SkhI0Nq1a91jTz75pCIjI5WQkKAWLVpo69at3rnDAAAA8BqvRW9SUpK6deumdu3aSZLS09MVExMjm80mSQoPD9fu3bslSWlpaerQoYN73xuN7dq1S5Zlad++fdccKy2brfjFW641Fy5cqsrFm7x931kTLly4cKm4S0n4VOy31GvbtGmTNm7cqJUrVyoxMVGSlJ+fr5YtW7q3CQwMVFZWliTJ6XSqSZMmpRpzOp1yuVzFxg4dOlTq+davX7vU+1SE4OBa3p4CUCXx3CmONQFQ3Xg8ei9cuKApU6bopZdeUmBgoPt6h8MhPz8/99f+/v46f/58mcd8fC7ftavH/Pz83PuVxunTZ2VZRa9zOOwe/6GRk+NUYaHLo8cEypM3njdS5X7usCYAcHNstpK9QOnx6H333XfVpk0bdenSpcj1QUFBys7Odn/tdDrl6+tborErb1y7eqxGjRqqUaOGsrOz3XF99X6lYVkqFr3eUlnmAVQ1PHeKY00AVCcej96UlBTl5OQoMjJSknT+/Hl9/vnnkqR77rnHvd2ePXvUuHFjSVLbtm21Y8cODRgw4Jpj27dvV8eOHYuNtWnTRtu3b1fTpk0lSXv37nW/cQ4AAADVh8ffyDZ//nylpKRo2bJlWrZsmeLj4/XMM8/oH//4h7Zt26bU1FRdvHhRc+bMUVxcnCSpZ8+eWrVqldLT0+V0OpWcnFxkbP78+Tpx4oROnTqlxYsXFxmbM2eO8vPzdejQIX3xxRfuMQAAAFQfHn+l95Zbbinydc2aNRUcHKx69eppwoQJGj58uGrWrKnatWtr2rRpkqRWrVpp6NCh6t+/v/z9/dWsWTMNGjRIkhQfH6/Vq1erR48ekqSOHTu6//3444/ryy+/1M9//nMVFBRowIABatOmjQfvLQAAACoDr3x6w9WuhK0kDRw4UHFxccrIyFBkZKRq1fq/N3eMGTNGffr00YkTJxQVFeV+g5rNZtOMGTM0ZMgQnTt3TtHR0brysWd+fn6aO3eutm3bJj8/P4WHh3v2zgEAAKBS8Hr0/lhISIhCQkKuORYaGqrQ0NBrjv1U0Nrtdvf5wwAAAKievPpniAEAAABPIHoBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYrt+i1LEuFhYXldXMAAABAuSlT9L700ksqKCgoct2mTZv0wAMPlMukAAAAgPJUpuhduHBhsegNDQ3V0aNHy2VSAAAAQHnyKc3Gy5Ytk3T5VIaUlBQFBAS4v960aZPatGlT7hMEAAAAblaponfJkiWSJJvNppSUFDkcDkmS3W5X06ZN9eabb5b/DAEAAICbVKroTU5OliS1atVK77//vgIDAytkUgAAAEB5KtM5vb/4xS/k5+dX3nMBAAAAKkSpXum94uWXX1ZBQYGOHTsmy7KKjN12223lMjEAAACgvJQpeufNm6ekpCRdvHixSPTabDbt3bu33CYHAAAAlIcyRe/MmTP1/PPP67HHHpOvr295zwkAAAAoV2U6p7dWrVrq0KEDwQsAAIAqoUzRO3HiRE2aNEn79+8v7/kAAAAA5a5MpzckJiYqNzdXffv2VZ06dYp8dNmXX35ZbpMDAAAAykOZonfatGnlPQ8AAACgwpQpeps0aVLe8wAAAAAqTJmiNz4+Xjabzf1xZTabzT3GR5YBAACgsilT9Kalpbn/ff78ee3atUuzZs3SsGHDym1iAAAAQHkp06c3XK1GjRqKjIzUn/70J/3xj38sjzkBAAAA5eqmo/eK3NxcnT59urxuDgAAACg3N3VO7xWWZSkrK0tDhw4tt4kBAAAA5aVcPrLMZrOpcePGatq0ablMCgAAAChPZTq9ITo6WtHR0apRo4ays7Pl7+9P8AIAAKDSKtMrvSdOnNDIkSP17bffqnHjxsrKylLz5s317rvvqnHjxuU9RwAAAOCmlOmV3smTJ6tNmzbatGmTPv/8c23cuFGtW7fWpEmTynt+AAAAwE0rU/R+/fXXGjlypPz8/CRJfn5+GjFihLZt21aukwMAAADKQ5mi984779Rnn31W5LrPPvtMd9xxR7lMCgAAAChPZTqn96WXXtKTTz6plJQUNWnSRJmZmXI6nfrzn/9c3vMDAAAAblqZovfOO+/UF198oX/84x86duyYHn74YXXp0kU1a9Ys7/kBAAAAN61MpzccOHBAgwcPlt1u13//93/rvffe02OPPaZDhw6V9/wAAACAm1bmT2+IiopSXFycJGnhwoXq0qWLpkyZUq6TAwAAAMpDmaJ37969+u///m/Vrl1bklSzZk0NGTJEu3fvLtfJAQAAAOWhTNEbFhZW7NMbli9fzqc3AAAAoFIq0xvZJk+erKeeekrLly9XkyZNdPToUeXl5WnOnDnlPT8AAADgppUpeu+++2598cUX+uc//6ljx46pb9++6tKliwIDA8t7fgAAAMBNK1P0SlJgYKAefPDB8pwLAAAAUCHKdE4vAAAAUJUQvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAON5LXrPnDmjb775Rnl5ed6aAgAAAKoJr0Tv559/rvj4eE2cOFGdO3fW559/Lknat2+f+vfvr6ioKCUlJcmyLPc+mzdvVq9evRQTE6O5c+cWub3Vq1era9euiouL08qVK4uMzZs3T7GxsUpISNDGjRsr/s4BAACg0vF49J49e1Yvv/yy/vKXvyglJUWTJ0/WjBkzVFBQoBEjRqh169ZasmSJDh48qKVLl0qSsrOzNXLkSPXu3VsLFy5USkqKNm3aJOlyKI8fP16jRo3SBx98oJkzZyojI0OStH79eiUlJWnq1KmaMWOGJk6cqJycHE/fZQAAAHiZx6M3Pz9fL7zwglq1aiVJuvvuu5WTk6N169YpPz9fEyZMUNOmTTV27FgtXrxYkrRixQo1atRIo0ePVvPmzTVq1Cj32KJFixQTE6MBAwYoLCxMgwcP1vLlyyVJCxYsUL9+/dStWze1b99eCQkJWrt2rafvMgAAALzMx9MHvPXWW9W3b19J0sWLF/XRRx+pe/fuSktLU0REhAICAiRJYWFhOnjwoCQpPT1dMTExstlskqTw8HC98cYbkqS0tDT9/Oc/d99+eHi43nnnHffY+PHji4xt2bJFAwYMKNWc//ewlUJlmgtQlfDcKY41AWCCkn4v83j0XpGWlqZhw4bJ19dXq1at0rvvvqsmTZq4x202m+x2u/Ly8pSfn6+WLVu6xwIDA5WVlSVJcjqdRfYr6Vhp1K9fu9T7VITg4FrengJQJfHcKY41AVDdeC16w8LC9MEHH+i1117TxIkTFRISIj8/vyLb+Pv76/z583I4HEXGrlwvqcxjpXH69Fld9Z66/71tu8d/aOTkOFVY6PLoMYHy5I3njVS5nzusCQDcHJutZC9Qei16bTab2rRpo2nTpql79+4aO3as9u/fX2Qbp9MpX19fBQUFKTs7u9j1km44dvUb164eKw3LUrHo9ZbKMg+gquG5UxxrAqA68fgb2TZv3qykpCT3135+frLZbGrZsqV27Njhvj4zM1MFBQUKCgpS27Zti4zt2bNHjRs3lqQbjm3fvv2aYwAAAKg+PB69zZs316effqqFCxfq2LFjevPNN9WpUyd17txZ+fn5WrJkiSRp9uzZio2NlcPhUHx8vLZt26bU1FRdvHhRc+bMUVxcnCSpZ8+eWrVqldLT0+V0OpWcnFxkbP78+Tpx4oROnTqlxYsXu8cAT7DbbfLxsXvsYrfzziQAAK7F46c3NGrUSDNnztSrr76qpKQkxcXFafr06fLx8VFiYqLGjRun6dOny263Kzk5WZJUr149TZgwQcOHD1fNmjVVu3ZtTZs2TZLUqlUrDR06VP3795e/v7+aNWumQYMGSZLi4+O1evVq9ejRQ5LUsWNH97+Bima321S3bk05HJ77f8vCQpdyc3+Qy8XvrQEAuJpXzunt1KmT/vrXvxa7PiEhQWvWrNHu3bsVERGh4OBg99jAgQMVFxenjIwMRUZGqlat/3vjx5gxY9SnTx+dOHFCUVFR7jev2Ww2zZgxQ0OGDNG5c+cUHR3t/tgzoKLZ7TY5HHZNnL9eh7Iq/s9tt2gUpMRB98lutxG9AAD8iNfeyPZTGjZsqC5dulxzLCQkRCEhIdccCw0NVWho6DXHwsPDy2t6QKkdyspT2tHsG28IAAAqjMfP6QUAAAA8jegFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABiP6AUAAIDxiF4AAAAYj+gFAACA8YheAAAAGI/oBQAAgPGIXgAAABjPx9sTAADgx+x2m+x2m8eO53JZcrksjx0PgOcRvQCASsVut6lu3ZpyODz3y8jCQpdyc38gfAGDEb0AgErFbrfJ4bBr4vz1OpSVV+HHa9EoSImD7pPdbiN6AYMRvQCASulQVp7SjmZ7exoADMEb2QAAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPKIXAAAAxiN6AQAAYDyiFwAAAMYjegEAAGA8ohcAAADGI3oBAABgPK9E79q1a5WQkKC7775bDz30kA4ePChJ2rdvn/r376+oqCglJSXJsiz3Pps3b1avXr0UExOjuXPnFrm91atXq2vXroqLi9PKlSuLjM2bN0+xsbFKSEjQxo0bK/7OAQAAoNLxePQeOXJEL7zwgsaNG6d169apefPmevHFF1VQUKARI0aodevWWrJkiQ4ePKilS5dKkrKzszVy5Ej17t1bCxcuVEpKijZt2iTpciiPHz9eo0aN0gcffKCZM2cqIyNDkrR+/XolJSVp6tSpmjFjhiZOnKicnBxP32UAAG6a3W6Tj4/dYxe73ebtuwyUKx9PH/DgwYMaN26cHnjgAUnSwIED9fTTT2vdunXKz8/XhAkTFBAQoLFjx+rll19W//79tWLFCjVq1EijR4+WzWbTqFGjtHjxYnXo0EGLFi1STEyMBgwYIEkaPHiwli9frjFjxmjBggXq16+funXrJklKSEjQ2rVr3dsCAFAV2O021a1bUw6H516rKix0KTf3B7lc1o03BqoAj0dv165di3x96NAhNWvWTGlpaYqIiFBAQIAkKSwszH3aQ3p6umJiYmSzXf6/zvDwcL3xxhuSpLS0NP385z933154eLjeeecd99j48eOLjG3ZsqXU0WurRP+zW5nmgsqLx0lxrElxrElxlXVN7HabHA67Js5fr0NZeRV+vBaNgpQ46D7Z7bYipxoClVFJn7cej96rFRQUaO7cuXriiSd05MgRNWnSxD1ms9lkt9uVl5en/Px8tWzZ0j0WGBiorKwsSZLT6SyyX0nHSqN+/dql3qciBAfX8vYUUAXwOCmONSmONSmuKqzJoaw8pR3N9tjxqsKaACXl1eidNWuWAgICNGDAAL399tvy8/MrMu7v76/z58/L4XAUGbtyvaQyj5XG6dNn9eP/0XU47B7/ZpCT41Rhocujx0TZeeMxIlXuxwlrUhxrUhxrUhxrAvw0m61kL1B6LXo3btyoefPm6dNPP5Wvr6+CgoK0f//+Its4nU73WHZ2drHrJd1w7Oo3rl09VhqWpWLR6y2VZR6o3HicFMeaFMeaFMeaFMeawBRe+ciyzMxMjRs3TpMnT1ZoaKgkqW3bttqxY0eRbQoKChQUFFRsbM+ePWrcuPE19/vx2Pbt2685BgAAgOrD49F7/vx5jRgxQgkJCerevbucTqecTqciIyOVn5+vJUuWSJJmz56t2NhYORwOxcfHa9u2bUpNTdXFixc1Z84cxcXFSZJ69uypVatWKT09XU6nU8nJyUXG5s+frxMnTujUqVNavHixewwAAADVh8dPb9iwYYMOHDigAwcO6NNPP3Vf/+WXXyoxMVHjxo3T9OnTZbfblZycLEmqV6+eJkyYoOHDh6tmzZqqXbu2pk2bJklq1aqVhg4dqv79+8vf31/NmjXToEGDJEnx8fFavXq1evToIUnq2LGj+98AAACoPjwevd26dVN6evo1x5o0aaI1a9Zo9+7dioiIUHBwsHts4MCBiouLU0ZGhiIjI1Wr1v+d0D9mzBj16dNHJ06cUFRUlPvNazabTTNmzNCQIUN07tw5RUdHuz/2DAAAANWHVz+94VoaNmyoLl26XHMsJCREISEh1xwLDQ11nx/8Y+Hh4eU1PQAAAFRBXnkjGwAAAOBJRC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHhELwAAAIzntejNzs5WfHy8vvvuO/d1+/btU//+/RUVFaWkpCRZluUe27x5s3r16qWYmBjNnTu3yG2tXr1aXbt2VVxcnFauXFlkbN68eYqNjVVCQoI2btxYsXcKAAAAlZJXojc7O1sjRozQ0aNH3dcVFBRoxIgRat26tZYsWaKDBw9q6dKl7u1Hjhyp3r17a+HChUpJSdGmTZskXQ7l8ePHa9SoUfrggw80c+ZMZWRkSJLWr1+vpKQkTZ06VTNmzNDEiROVk5Pj+TsMAAAAr/JK9I4dO1YPPvhgkevWrVun/Px8TZgwQU2bNtXYsWO1ePFiSdKKFSvUqFEjjR49Ws2bN9eoUaPcY4sWLVJMTIwGDBigsLAwDR48WMuXL5ckLViwQP369VO3bt3Uvn17JSQkaO3atZ69swAAAPA6r0TvK6+8oqFDhxa5Li0tTREREQoICJAkhYWF6eDBg5Kk9PR0xcTEyGazSZLCw8O1e/du934dOnRw386Nxnbt2lXq+dpsxS/ecq25cKmcF2/y9n1nTVgT1oQ14cLFk5eS8KnYp8q1hYSEFLsuPz9fTZo0cX9ts9lkt9uVl5en/Px8tWzZ0j0WGBiorKwsSZLT6SyyX0nHSqN+/dql3qciBAfX8vYUUAXwOCmONSmONSmONSmONYFJvBK91+JwOOTn51fkOn9/f50/f77Y2JXrr7VfScdK4/Tps7rqPXX/e9t2j38zyMlxqrDQ5dFjouy88RiRKvfjhDUpjjUpjjUpjjUBfprNVrIXKCtN9AYFBWn//v1FrnM6nfL19VVQUJCys7OLXX9lv+uNXf3GtavHSsOyVCx6vaWyzAOVG4+T4liT4liT4liT4lgTmKLSfE5v27ZttWPHDvfXmZmZKigoUFBQULGxPXv2qHHjxtfc78dj27dvv+YYAAAAqo9KE71RUVHKz8/XkiVLJEmzZ89WbGysHA6H4uPjtW3bNqWmpurixYuaM2eO4uLiJEk9e/bUqlWrlJ6eLqfTqeTk5CJj8+fP14kTJ3Tq1CktXrzYPQYAAIDqo9Kc3uDj46PExESNGzdO06dPl91uV3JysiSpXr16mjBhgoYPH66aNWuqdu3amjZtmiSpVatWGjp0qPr37y9/f381a9ZMgwYNkiTFx8dr9erV6tGjhySpY8eO7n8DAACg+vBq9Kanpxf5OiEhQWvWrNHu3bsVERGh4OBg99jAgQMVFxenjIwMRUZGqlat/zuhf8yYMerTp49OnDihqKgo95vXbDabZsyYoSFDhujcuXOKjo6WraSfawEAAABjVJpXeq9o2LChunTpcs2xkJCQa37cmSSFhoYqNDT0mmPh4eHlNT0AAABUQZXmnF4AAACgohC9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMJ6PtycAABXN4fDM/9+7XJZcLssjxwIAlA7RC8BY9WvXkOUqVJ06AR45nquwUDm55whfAKiEiF4Axqpdw082u0Onlv5OF09lVOixfBv8TA0emSa73Ub0Ah5it9tkt9s8cix+k1P1Eb0AjHfxVIYuHt/r7WkAKEd2u01169b02OlLhYUu5eb+QPhWYUQvAACocux2mxwOuybOX69DWXkVeqwWjYKUOOg+fpNTxRG9AACgyjqUlae0o9nengaqAKIXAKohPtECQHVD9FYhnvohJfGDCjAVn2gBoLoieqsAT/+QkvhBBZiKT7QAUF0RvVWAJ39ISfygAqoDPtECQHVD9FYh/JACAAAoG8+dJAoAAAB4CdELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOPxZ4gBAJDkcHjmdSCXy5LLZXnkWAD+D9ELAKjW6teuIctVqDp1AjxyPFdhoXJyzxG+gIcRvYBhPPVqlcQrVjBD7Rp+stkdOrX0d7p4KqNCj+Xb4Gdq8Mg02e22KvHc4dVvmIToBQzh6VerJF6xglkunsrQxeN7vT2NSoFXv2EiohcwhCdfrZKq3itWAEqOV79hIqIXMAyvVgEoL3w/gUn4yDIAAAAYj+gFAACA8YheAAAAGI/oBQAAgPF4IxsAAIAh7Hab7HabR45V1T5fmegFAAAwgN1uU926NT32R0UKC13Kzf2hyoQv0QsAAGAAu90mh8OuifPX61BWXoUeq0WjICUOuq9Kfb5ytYjeffv2acKECTpy5IgeffRRPffcc7LZPPPSPwAAMENV+TPvh7LylHY0u5xnVPUZH70FBQUaMWKE4uLi9NZbbykxMVFLly5V//79vT01AABQBfBn3s1gfPSuW7dO+fn5mjBhggICAjR27Fi9/PLLRC8AACgR/sz7T6sqr35L1SB609LSFBERoYCAy/93FhYWpoMHD5bqNux2yfqJNW51Wz0F+FXsMjZrVEeS5HfLXbL5Vvz/ZfrWb+7+t72Uj2WbzeaxU0csy5L1U/9hKhFPPEakqvU4MXFNbmY9JNbkWliT4liT4jz5c9jm4++R7682H3/3vyvrmrRt2sDjr35bhYXKyTtX7Gd/SbPDZlWFargJ06ZN04ULFzRlyhT3dR06dNAXX3yhoKAgL84MAAAAnmL8H6dwOBzy8/Mrcp2/v7/Onz/vpRkBAADA04yP3qCgIGVnF30Ho9PplK+vr5dmBAAAAE8zPnrbtm2rHTt2uL/OzMxUQUEBpzYAAABUI8ZHb1RUlPLz87VkyRJJ0uzZsxUbGyuHw+HlmQEAAMBTjH8jmyR9+eWXGjdunPz9/WW325WcnKzQ0FBvTwsAAAAeUi2iV5JOnjyp3bt3KyIiQsHBwd6eDgAAADyo2kQvAAAAqi/jz+kFAAAAiF4AAAAYj+gFUKWdOXNG33zzjfLy8rw9FQBAJUb0VgL79u1T//79FRUVpaSkpGJ/U/paVq9era5duyouLk4rV670wCw9Lzs7W/Hx8fruu+9KtP3mzZvVq1cvxcTEaO7cuRU8O89bu3atEhISdPfdd+uhhx7SwYMHb7iP6Wvy+eefKz4+XhMnTlTnzp31+eef33Af09fkak8++aSWLl16w+1M/36SmJiosLAw96V79+433Ke6PE5mzJihESNGlGhbk9dk6dKlRR4jVy43ev6Y/NxZtGiROnfurIiICA0ZMkSZmZk33KfSP0YseNWFCxesrl27WpMmTbIOHz5sPfXUU9bixYuvu096errVunVr69NPP7XS0tKs7t27WwcPHvTQjD3j9OnT1oABA6w777zTyszMLNH27du3t2bNmmUdOnTIevjhh62NGzd6YKaecfjwYSsqKsr661//ap08edJ65plnrF/84hfX3cf0NTlz5owVExNj7d2717Isy1qyZInVtWvX6+5j+ppcbfny5dadd95pLVmy5LrbVYfvJ7/4xS+sf/7zn1ZeXp6Vl5dnnT179rrbV5fHyd69e6127dpZR44cueG2pq/JhQsX3I+PvLw869ixY1ZMTIx1+PDhn9zH5OfO4cOHrc6dO1u7du2yjh49ak2YMMEaNGjQdfepCo8RXun1snXr1ik/P18TJkxQ06ZNNXbsWC1evPi6+yxatEgxMTEaMGCAwsLCNHjwYC1fvtxDM/aMsWPH6sEHHyzx9itWrFCjRo00evRoNW/eXKNGjbrhOlYlBw8e1Lhx4/TAAw+oQYMGGjhwoPbu3XvdfUxfk/z8fL3wwgtq1aqVJOnuu+9WTk7OdfcxfU2uyM3NVVJSklq0aHHDbU3/fnLp0iXt379fkZGRqlOnjurUqaPAwMDr7lMdHicul0uTJ0/WE088oZCQkBtub/qa+Pn5uR8fderU0bJly9S9e3c1bdr0J/cx+bmzZ88eRUREqHXr1rrtttvUv39/HT58+Lr7VIXHCNHrZWlpaYqIiFBAQIAkKSws7Ia/tk5LS1OHDh3cX4eHh2v37t0VOk9Pe+WVVzR06NASb5+enq6YmBjZbDZJ5q1J165d9Ytf/ML99aFDh9SsWbPr7mP6mtx6663q27evJOnixYv66KOPbvhra9PX5IqkpCR169ZN7dq1u+G2pn8/2bdvn1wul/r166fw8HA9+eST+v7776+7T3V4nCxYsED79u3T7bffri+//FIFBQXX3b46rMkVFy5c0Mcff6ynn376utuZ/NwJDQ3Vpk2btHfvXp09e1bz589Xp06drrtPVXiMEL1elp+fryZNmri/ttlsstvt131TjtPpLLJPYGCgsrKyKnSenlaSVx6u9uN1NHFNrigoKNDcuXP1+OOPX3e76rImaWlpiouL0/r16zVx4sTrblsd1mTTpk3auHGjfvvb35Zoe9O/nxw4cEAtWrTQ9OnTtWLFCvn4+GjSpEnX3cf0x4nT6dSsWbMUEhKi77//Xh9++KEGDRqk8+fP/+Q+pq/J1VJSUhQeHl7k/l6Lyc+d0NBQ9ezZU/369VNkZKR27Nih559//rr7VIXHCNHrZQ6HQ35+fkWu8/f3v+43nx/vc6Ptq4PqtCazZs1SQECABgwYcN3tqsuahIWF6YMPPlCzZs1uGL2mr8mFCxc0ZcoUvfTSSzf8Ff4Vpq9J3759tXTpUt1zzz1q3ry5pkyZotTUVOXn5//kPqavyZo1a3Tu3Dl99NFHeuaZZzR37lw5nc7r/mre9DW52ieffKKBAwfecDuT12Tnzp36xz/+oU8//VRbt27Vgw8+qKeeeuq6b7SvCutB9HpZUFCQsrOzi1zndDrl6+tb4n1utH11UF3WZOPGjZo3b57eeOONG96/6rImNptNbdq00bRp0/S3v/1NZ86c+cltTV+Td999V23atFGXLl1KvI/pa/Jj9evXl8vluu4rUKavyfHjxxUREaF69epJknx8fBQWFnbdczZNX5MrDh8+rCNHjig2NvaG25q8JitXrlTv3r0VERGh2rVr69lnn1VmZqbS0tJ+cp+qsB5Er5e1bdtWO3bscH+dmZmpgoICBQUFlXifPXv2qHHjxhU4y8qvOqxJZmamxo0bp8mTJys0NPSG25u+Jps3b1ZSUpL7az8/P/fpQT/F9DVJSUnR3//+d0VGRioyMlIrV67Uyy+/rJdeeukn9zF9TZKSkpSSkuL+evv27bLb7br11lt/ch/T1+SWW27RhQsXilz3/fffX/c+mr4mV3z++efq0qVLiWLN5DVxuVw6ffq0+2un06lz586psLDwJ/epCutB9HpZVFSU8vPztWTJEknS7NmzFRsbK4fDoTNnzlzzAdazZ0+tWrVK6enpcjqdSk5OVlxcnKen7hX5+fm6ePFisevj4+O1bds2paam6uLFi5ozZ45Ra3L+/HmNGDFCCQkJ6t69u5xOp5xOpyzLqrZr0rx5c3366adauHChjh07pjfffFOdOnVSYGBgtV2T+fPnKyUlRcuWLdOyZcsUHx+vZ555Rs8880y1/X7SqlUrvf3229q4caM2bNigKVOmqF+/fgoICKi2j5POnTvrwIEDWrBggY4fP66PP/5YaWlp6tGjR7VdkyvWr1+v6OjoItdVx+dOZGSk1qxZow8//FApKSkaNWqUGjZsqLCwsKr9GPH2Z6bBstauXWtFRERY0dHRVocOHaz9+/dblmVZd955p7Vnz55r7vPmm29arVu3ttq3b289/PDD1rlz5zw5ZY/58ef0du3a1VqzZs01t50/f77VunVrKyoqyoqPj7dOnjzpqWlWuDVr1lh33nlnsUtmZma1XRPLsqwNGzZYDzzwgHXPPfdYv/nNb6zTp09bllV9Hyc/9vzzz7s/p7c6fz95/fXXrXvvvdeKjo62XnnlFcvpdFqWVb0fJ1u3brUee+wxKzw83EpISLC+/PJLy7Kq95qcO3fOat26tXXgwIEi11fH547L5bL++Mc/Wl26dLFat25t9evXz9q9e7dlWVX7MWKzrBL8+S9UuJMnT2r37t2KiIhQcHBwifY5cOCATpw4oaioqGJvhquuMjMzlZGRocjISNWqVcvb06kUWJPiWJPi+H5SHI+T4liT4njuFFWZHyNELwAAAIzHOb0AAAAwHtELAAAA4xG9AAAAMB7RCwAAAOMRvQAAADAe0QsAAADjEb0AAAAwHtELAAAA4xG9AOAlX3/9tfr166eIiAg9+uijOnDggCQpLS1NDz74oKKjo/Xqq6/q/vvvV3JysiRp3bp16tOnjyIjI/Xiiy+qoKCgRMeKj49Xamqq3njjDXXq1En79+93j3366afq0qWL7rnnHv3617+W0+mUJM2aNUu/+93v9Mc//lGRkZGKj4/X119/7d5v5cqV6tSpkxISEpSUlKSOHTsqLS1NlmVpzpw56tq1q+Li4vTRRx+V15IBQJkRvQDgBS6XS//zP/+j7t27a+3atYqKilJSUpIk6aWXXlLv3r318ccfa/Hixfr973+vvn376vDhwxo1apSGDh2qxYsXa+fOnZozZ06Jj/mHP/xBx48f1+uvv67bb79dkrR//3699NJLSkxM1KpVq5Sbm6v58+e79/nqq6/03Xff6bPPPlP79u315ptvSpLy8vI0adIkvf766/rNb36jlJQULVu2TC1atNDy5cs1e/ZsvfHGG3r77bf11ltvaevWreW4egBQekQvAHjJZ599pqeeekrHjh3TmTNndOjQIUnS3r17df/996tVq1a64447dOzYMQUFBWnVqlW66667NGDAADVv3lyDBg3S3//+9xIfLzAwUDNmzFDHjh1Vs2ZNSVLTpk21YcMG3XPPPfr222916dIl9zwkyeFwaOrUqQoJCVG/fv107NgxSdLhw4dVq1YtdezYUQ888IBOnjwpm80mf39/LV++XI899pjat2+vyMhIde3atVTzBICK4OPtCQBAdWS32/Xhhx9q8eLFCgkJ0a233iqXyyXpcoju2LFDwcHB+vbbbxUaGipJOn78uPbu3avIyEhJUmFhoTteS2LIkCHFrjt//rwmTZqkrVu3qlWrVrLb7e55SFK7du3k5+cnSfL19XVff/vtt+vMmTM6cuSIjh8/rrp166pBgwbueW7btk0LFy6UJF24cEHdunUrzfIAQLkjegHAC/79739r8eLFWrVqlerXr6+vvvpKu3fvlmVZCg0NVWJioiZNmqTBgwerVatWkqRbbrlFXbt21fPPPy/p8ikS586dK/ExAwICil338ccf6+zZs9qwYYN8fX01ffp0ZWdnu8cDAwOveVu+vr762c9+pt69e8vhcOjVV1+V3W53z7N///66//77JUkFBQVFghkAvIHTGwDAC668WezMmTP6+uuv9dprr8myLGVkZGjr1q1asGCB1qxZowkTJrj36d27t7Zu3arDhw/Lz89PycnJRcbLOg/LspSdna2UlBQtWLBAlmXdcL+//vWvaty4sZYtW6avvvpKDzzwgHvsoYce0qpVq9y3PXny5CLnCQOANxC9AOAF9913n+Li4vTII4/opZde0mOPPaasrCwFBQWpfv36GjJkiBISEhQeHq7JkydLunzaQ1JSkqZNm6YePXooPT3d/caysho2bJgKCgp0//33a+nSpXr00Ue1d+/eG+7XuXNnbdu2TY8//rhiYmLUoUMHLVq0SNLl6O3Vq5eefvppPfzww2rSpImeeeaZm5onANwsm1WS/6UHAHjEokWLtHr1aiUmJqpGjRpKT0/X8OHDlZqa+pOnGnjDb3/7W912220aMmSIbDabVqxYoZSUFC1dutTbUwOAa+KcXgCoRDp06KCVK1fqwQcf1IULF3Tbbbdp3Lhx1w3erVu3asSIEdcce+yxx/Tcc8+V+zz79Omj119/XR9++KEkKTQ0VL/97W/L/TgAUF54pRcAqrgLFy7o5MmT1xwLDAxU3bp1PTshAKiEiF4AAAAYjzeyAQAAwHhELwAAAIxH9AIAAMB4RC8AAACMR/QCAADAeEQvAAAAjEf0AgAAwHj/HwIkn84Rc7doAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 355
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:00.117285Z",
     "start_time": "2024-09-19T12:04:59.814471Z"
    }
   },
   "cell_type": "code",
   "source": [
    "repeat_buy = [rate for rate in train_data_user_info.groupby(['age_range'])['label'].mean()]\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "sns.distplot(repeat_buy, fit=stats.norm)\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "res = stats.probplot(repeat_buy, plot=plt)"
   ],
   "id": "881cc409c7317126",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 356
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:07.001550Z",
     "start_time": "2024-09-19T12:05:00.121294Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_1 = user_log.merge(train_data, on=['user_id'], how='left')\n",
    "all_data_1[all_data_1['label'].notnull()].head()"
   ],
   "id": "143c2fb072dadf68",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     user_id  item_id  cat_id  seller_id  brand_id  time_stamp  action_type  \\\n",
       "419   234512   146770    1173        693    3186.0         625            0   \n",
       "420   234512   146770    1173        693    3186.0         625            0   \n",
       "421   234512  1106076     992       3783    8164.0        1016            0   \n",
       "422   234512  1106076     992       3783    8164.0        1016            0   \n",
       "423   234512   866567    1198        693    3186.0         625            0   \n",
       "\n",
       "     merchant_id  label  \n",
       "419       3018.0    0.0  \n",
       "420       3271.0    0.0  \n",
       "421       3018.0    0.0  \n",
       "422       3271.0    0.0  \n",
       "423       3018.0    0.0  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>action_type</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>234512</td>\n",
       "      <td>146770</td>\n",
       "      <td>1173</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>234512</td>\n",
       "      <td>1106076</td>\n",
       "      <td>992</td>\n",
       "      <td>3783</td>\n",
       "      <td>8164.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "      <td>3271.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>234512</td>\n",
       "      <td>866567</td>\n",
       "      <td>1198</td>\n",
       "      <td>693</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>625</td>\n",
       "      <td>0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 357
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:09.037308Z",
     "start_time": "2024-09-19T12:05:07.002464Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2 = all_data_1[all_data_1['label'].notnull()]\n",
    "all_data_2_sum = all_data_2.groupby(['time_stamp'])['label'].sum().reset_index()\n",
    "all_data_2_sum.head()"
   ],
   "id": "7ec2fe43707494cc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   time_stamp   label\n",
       "0         511   943.0\n",
       "1         512   975.0\n",
       "2         513  1221.0\n",
       "3         514  1170.0\n",
       "4         515  1260.0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_stamp</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
       "      <td>943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>512</td>\n",
       "      <td>975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513</td>\n",
       "      <td>1221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>514</td>\n",
       "      <td>1170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>515</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 358
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:09.044862Z",
     "start_time": "2024-09-19T12:05:09.038820Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum['time_stamp'] = all_data_2_sum['time_stamp'].astype(str)\n",
    "all_data_2_sum['label'] = all_data_2_sum['label'].astype(int)\n",
    "a = []\n",
    "for i in range(len(all_data_2_sum)):\n",
    "    if len(all_data_2_sum['time_stamp'][i]) == 3:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0])\n",
    "    else:\n",
    "        a.append(all_data_2_sum['time_stamp'][i][0:2])"
   ],
   "id": "f866fb7993c75335",
   "outputs": [],
   "execution_count": 359
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:10.148181Z",
     "start_time": "2024-09-19T12:05:09.045372Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data_2_sum['month'] = a\n",
    "all_data_2_sum = all_data_2_sum.astype(int)\n",
    "plt.figure(figsize=(20, 8))\n",
    "c = 5\n",
    "for i in range(1, 8):\n",
    "    plt.subplot(3, 3, i)\n",
    "    b = all_data_2_sum[all_data_2_sum['month'] == c]\n",
    "    plt.plot(b['time_stamp'], b['label'], linewidth=1, color='red', marker='o', label='Mean value')\n",
    "    c += 1"
   ],
   "id": "109534972b8e20a8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 7 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 360
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 2.特征工程",
   "id": "e97c57646dc09034"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:10.511729Z",
     "start_time": "2024-09-19T12:05:10.150187Z"
    }
   },
   "cell_type": "code",
   "source": [
    "if 'prob' in test_data.columns:\n",
    "    del test_data['prob']\n",
    "test_data['target'] = 1\n",
    "train_data['target'] = 1\n",
    "# 被删除了，改为全连接\n",
    "# all_data = train_data.append(test_data)  \n",
    "# 使用 concat 函数来合并 train_data 和 test_data\n",
    "all_data = pd.concat([train_data, test_data])\n",
    "all_data = all_data.merge(user_info, on='user_id', how='left')\n",
    "del train_data, test_data, user_info\n",
    "gc.collect()"
   ],
   "id": "2f8def357fdfcf58",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42743"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 361
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:10.520920Z",
     "start_time": "2024-09-19T12:05:10.512734Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "af3087bbf256a192",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender\n",
       "0    34176         3906    0.0       1        6.0     0.0\n",
       "1    34176          121    0.0       1        6.0     0.0\n",
       "2    34176         4356    1.0       1        6.0     0.0\n",
       "3    34176         2217    0.0       1        6.0     0.0\n",
       "4   230784         4818    0.0       1        0.0     0.0"
      ],
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
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       "      <td>34176</td>\n",
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       "      <td>6.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 362
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:10.553887Z",
     "start_time": "2024-09-19T12:05:10.521925Z"
    }
   },
   "cell_type": "code",
   "source": [
    "all_data.dropna(subset=['age_range', 'gender'], inplace=True)\n",
    "all_data.isnull().sum()"
   ],
   "id": "f35f35e90ea45b36",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             0\n",
       "merchant_id         0\n",
       "label          257626\n",
       "target              0\n",
       "age_range           0\n",
       "gender              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 363
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:11.519861Z",
     "start_time": "2024-09-19T12:05:10.554891Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#用户店铺数\n",
    "sell = user_log.groupby(['user_id'])['seller_id'].count().reset_index()\n",
    "all_data = all_data.merge(sell, on='user_id', how='inner')\n",
    "all_data.rename(columns={'seller_id': 'sell_sum'}, inplace=True)\n",
    "all_data.head()"
   ],
   "id": "2ff72906b50b14e2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum\n",
       "0    34176         3906    0.0       1        6.0     0.0       451\n",
       "1    34176          121    0.0       1        6.0     0.0       451\n",
       "2    34176         4356    1.0       1        6.0     0.0       451\n",
       "3    34176         2217    0.0       1        6.0     0.0       451\n",
       "4   230784         4818    0.0       1        0.0     0.0        54"
      ],
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       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
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       "  </thead>\n",
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       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
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       "      <td>4356</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 364
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:16.161006Z",
     "start_time": "2024-09-19T12:05:11.520866Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def nunique_k(data, sigle_name, new_name_1):\n",
    "    data1 = user_log.groupby(['user_id'])[sigle_name].nunique().reset_index()\n",
    "    data_union = data.merge(data1, on=['user_id'], how='inner')\n",
    "    data_union.rename(columns={sigle_name: new_name_1}, inplace=True)\n",
    "    return data_union\n",
    "\n",
    "\n",
    "#不同店铺个数\n",
    "all_data = nunique_k(all_data, 'seller_id', 'seller_unique')"
   ],
   "id": "43ae62d602bf3a67",
   "outputs": [],
   "execution_count": 365
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:16.182532Z",
     "start_time": "2024-09-19T12:05:16.163019Z"
    }
   },
   "cell_type": "code",
   "source": "all_data",
   "id": "9225ee5f4a32003b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN       1        6.0     0.0      2004   \n",
       "514763    97919         2341    NaN       1        8.0     1.0        55   \n",
       "514764    97919         3971    NaN       1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN       1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN       1        0.0     0.0        72   \n",
       "\n",
       "        seller_unique  \n",
       "0                 109  \n",
       "1                 109  \n",
       "2                 109  \n",
       "3                 109  \n",
       "4                  20  \n",
       "...               ...  \n",
       "514762            278  \n",
       "514763             17  \n",
       "514764             17  \n",
       "514765             33  \n",
       "514766             33  \n",
       "\n",
       "[514767 rows x 8 columns]"
      ],
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
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       "      <td>17</td>\n",
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       "      <th>514764</th>\n",
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       "      <td>3971</td>\n",
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       "  </tbody>\n",
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       "<p>514767 rows × 8 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 366
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:25.491009Z",
     "start_time": "2024-09-19T12:05:16.183540Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#不同商品个数\n",
    "all_data = nunique_k(all_data, 'cat_id', 'cat_id_unique')\n",
    "#活跃天数\n",
    "all_data = nunique_k(all_data, 'time_stamp', 'time_stamp_unique')\n",
    "#不同行为种数\n",
    "all_data = nunique_k(all_data, 'action_type', 'action_type_unique')"
   ],
   "id": "f87cd5b32f0e19fb",
   "outputs": [],
   "execution_count": 367
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:25.504330Z",
     "start_time": "2024-09-19T12:05:25.492016Z"
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   },
   "cell_type": "code",
   "source": "all_data",
   "id": "55f1c709db443d8c",
   "outputs": [
    {
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      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
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       "514764    97919         3971    NaN       1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN       1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN       1        0.0     0.0        72   \n",
       "\n",
       "        seller_unique  cat_id_unique  time_stamp_unique  action_type_unique  \n",
       "0                 109             45                 47                   3  \n",
       "1                 109             45                 47                   3  \n",
       "2                 109             45                 47                   3  \n",
       "3                 109             45                 47                   3  \n",
       "4                  20             17                 16                   2  \n",
       "...               ...            ...                ...                 ...  \n",
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       "514764             17             14                  8                   3  \n",
       "514765             33             24                 15                   4  \n",
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       "[514767 rows x 11 columns]"
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      ]
     },
     "execution_count": 368,
     "metadata": {},
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   "execution_count": 368
  },
  {
   "metadata": {
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     "end_time": "2024-09-19T12:05:27.051470Z",
     "start_time": "2024-09-19T12:05:25.505337Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#活跃天数方差\n",
    "std = user_log.groupby(['user_id'])['time_stamp'].std().reset_index()\n",
    "all_data = all_data.merge(std, on='user_id', how='inner')\n",
    "all_data.rename(columns={'time_stamp': 'time_stamp_std'}, inplace=True)\n",
    "all_data.head()"
   ],
   "id": "d1b6907540f0d8d2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_unique  cat_id_unique  time_stamp_unique  action_type_unique  \\\n",
       "0            109             45                 47                   3   \n",
       "1            109             45                 47                   3   \n",
       "2            109             45                 47                   3   \n",
       "3            109             45                 47                   3   \n",
       "4             20             17                 16                   2   \n",
       "\n",
       "   time_stamp_std  \n",
       "0      206.449449  \n",
       "1      206.449449  \n",
       "2      206.449449  \n",
       "3      206.449449  \n",
       "4      218.341897  "
      ],
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     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 369
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:05:27.056983Z",
     "start_time": "2024-09-19T12:05:27.052475Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def most_love(data_1, most_name, new_name_2):\n",
    "    data2 = user_log.groupby(['user_id'])[most_name].apply(lambda x: Counter(x).most_common(1)[0][0]).reset_index()\n",
    "    data_union = data_1.merge(data2, on=['user_id'], how='inner')\n",
    "    data_union.rename(columns={most_name: new_name_2}, inplace=True)\n",
    "    return data_union"
   ],
   "id": "f8e589eec807808a",
   "outputs": [],
   "execution_count": 370
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:06:45.993335Z",
     "start_time": "2024-09-19T12:05:27.058518Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#用户最喜欢的店铺\n",
    "all_data = most_love(all_data, 'seller_id', 'seller_id_seller')\n",
    "#用户最喜欢的类目\n",
    "all_data = most_love(all_data, 'cat_id', 'cat_id_most')\n",
    "#用户最喜欢的品牌\n",
    "all_data = most_love(all_data, 'brand_id', 'brand_id_most')\n",
    "#用户最喜欢的行为\n",
    "all_data = most_love(all_data, 'action_type', 'action_type_most')"
   ],
   "id": "23dac2442b42194b",
   "outputs": [],
   "execution_count": 371
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:06:46.009096Z",
     "start_time": "2024-09-19T12:06:45.994340Z"
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   "cell_type": "code",
   "source": "all_data",
   "id": "b84a74ae0bc99ab",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN       1        6.0     0.0      2004   \n",
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       "514764    97919         3971    NaN       1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN       1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN       1        0.0     0.0        72   \n",
       "\n",
       "        seller_unique  cat_id_unique  time_stamp_unique  action_type_unique  \\\n",
       "0                 109             45                 47                   3   \n",
       "1                 109             45                 47                   3   \n",
       "2                 109             45                 47                   3   \n",
       "3                 109             45                 47                   3   \n",
       "4                  20             17                 16                   2   \n",
       "...               ...            ...                ...                 ...   \n",
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       "514765             33             24                 15                   4   \n",
       "514766             33             24                 15                   4   \n",
       "\n",
       "        time_stamp_std  seller_id_seller  cat_id_most  brand_id_most  \\\n",
       "0           206.449449               331          662         4094.0   \n",
       "1           206.449449               331          662         4094.0   \n",
       "2           206.449449               331          662         4094.0   \n",
       "3           206.449449               331          662         4094.0   \n",
       "4           218.341897              3556          407         1236.0   \n",
       "...                ...               ...          ...            ...   \n",
       "514762      156.030774              3017          662         7704.0   \n",
       "514763      196.860259               235          464         2020.0   \n",
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       "514765      172.779785              3319          898         5508.0   \n",
       "514766      172.779785              3319          898         5508.0   \n",
       "\n",
       "        action_type_most  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
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       "[514767 rows x 16 columns]"
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       "      <th>seller_id_seller</th>\n",
       "      <th>cat_id_most</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>3556</td>\n",
       "      <td>407</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514762</th>\n",
       "      <td>228479</td>\n",
       "      <td>3111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>278</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>156.030774</td>\n",
       "      <td>3017</td>\n",
       "      <td>662</td>\n",
       "      <td>7704.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
       "      <td>97919</td>\n",
       "      <td>3971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 16 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 372
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:06:46.014801Z",
     "start_time": "2024-09-19T12:06:46.010102Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def most_love_cnt(data_1, most_name, new_name_2):\n",
    "    data2 = user_log.groupby(['user_id'])[most_name].apply(lambda x: Counter(x).most_common(1)[0][1]).reset_index()\n",
    "    data_union = data_1.merge(data2, on=['user_id'], how='inner')\n",
    "    data_union.rename(columns={most_name: new_name_2}, inplace=True)\n",
    "    return data_union"
   ],
   "id": "e7920c1c259d55be",
   "outputs": [],
   "execution_count": 373
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:08:03.005195Z",
     "start_time": "2024-09-19T12:06:46.015808Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "#用户最喜欢的店铺次数\n",
    "all_data = most_love_cnt(all_data, 'seller_id', 'seller_id_cnt')\n",
    "#用户最喜欢的类目次数\n",
    "all_data = most_love_cnt(all_data, 'cat_id', 'cat_id_cnt')\n",
    "#用户最喜欢的品牌次数\n",
    "all_data = most_love_cnt(all_data, 'brand_id', 'brand_id_cnt')\n",
    "#用户最喜欢的行为次数\n",
    "all_data = most_love_cnt(all_data, 'action_type', 'action_type_cnt')"
   ],
   "id": "d075cfa0b6345241",
   "outputs": [],
   "execution_count": 374
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:08:03.024304Z",
     "start_time": "2024-09-19T12:08:03.007552Z"
    }
   },
   "cell_type": "code",
   "source": "all_data",
   "id": "9b887752e23b7f38",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0       1        6.0     0.0       451   \n",
       "1         34176          121    0.0       1        6.0     0.0       451   \n",
       "2         34176         4356    1.0       1        6.0     0.0       451   \n",
       "3         34176         2217    0.0       1        6.0     0.0       451   \n",
       "4        230784         4818    0.0       1        0.0     0.0        54   \n",
       "...         ...          ...    ...     ...        ...     ...       ...   \n",
       "514762   228479         3111    NaN       1        6.0     0.0      2004   \n",
       "514763    97919         2341    NaN       1        8.0     1.0        55   \n",
       "514764    97919         3971    NaN       1        8.0     1.0        55   \n",
       "514765    32639         3536    NaN       1        0.0     0.0        72   \n",
       "514766    32639         3319    NaN       1        0.0     0.0        72   \n",
       "\n",
       "        seller_unique  cat_id_unique  time_stamp_unique  action_type_unique  \\\n",
       "0                 109             45                 47                   3   \n",
       "1                 109             45                 47                   3   \n",
       "2                 109             45                 47                   3   \n",
       "3                 109             45                 47                   3   \n",
       "4                  20             17                 16                   2   \n",
       "...               ...            ...                ...                 ...   \n",
       "514762            278             71                110                   3   \n",
       "514763             17             14                  8                   3   \n",
       "514764             17             14                  8                   3   \n",
       "514765             33             24                 15                   4   \n",
       "514766             33             24                 15                   4   \n",
       "\n",
       "        time_stamp_std  seller_id_seller  cat_id_most  brand_id_most  \\\n",
       "0           206.449449               331          662         4094.0   \n",
       "1           206.449449               331          662         4094.0   \n",
       "2           206.449449               331          662         4094.0   \n",
       "3           206.449449               331          662         4094.0   \n",
       "4           218.341897              3556          407         1236.0   \n",
       "...                ...               ...          ...            ...   \n",
       "514762      156.030774              3017          662         7704.0   \n",
       "514763      196.860259               235          464         2020.0   \n",
       "514764      196.860259               235          464         2020.0   \n",
       "514765      172.779785              3319          898         5508.0   \n",
       "514766      172.779785              3319          898         5508.0   \n",
       "\n",
       "        action_type_most  seller_id_cnt  cat_id_cnt  brand_id_cnt  \\\n",
       "0                      0             70          98            70   \n",
       "1                      0             70          98            70   \n",
       "2                      0             70          98            70   \n",
       "3                      0             70          98            70   \n",
       "4                      0             10           9            10   \n",
       "...                  ...            ...         ...           ...   \n",
       "514762                 0            147         284           147   \n",
       "514763                 0             18          20            18   \n",
       "514764                 0             18          20            18   \n",
       "514765                 0             11          12            12   \n",
       "514766                 0             11          12            12   \n",
       "\n",
       "        action_type_cnt  \n",
       "0                   410  \n",
       "1                   410  \n",
       "2                   410  \n",
       "3                   410  \n",
       "4                    47  \n",
       "...                 ...  \n",
       "514762             1770  \n",
       "514763               46  \n",
       "514764               46  \n",
       "514765               62  \n",
       "514766               62  \n",
       "\n",
       "[514767 rows x 20 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>action_type_unique</th>\n",
       "      <th>time_stamp_std</th>\n",
       "      <th>seller_id_seller</th>\n",
       "      <th>cat_id_most</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_cnt</th>\n",
       "      <th>cat_id_cnt</th>\n",
       "      <th>brand_id_cnt</th>\n",
       "      <th>action_type_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
       "      <td>3906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34176</td>\n",
       "      <td>121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34176</td>\n",
       "      <td>4356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>206.449449</td>\n",
       "      <td>331</td>\n",
       "      <td>662</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>3556</td>\n",
       "      <td>407</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514762</th>\n",
       "      <td>228479</td>\n",
       "      <td>3111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>278</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>156.030774</td>\n",
       "      <td>3017</td>\n",
       "      <td>662</td>\n",
       "      <td>7704.0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>284</td>\n",
       "      <td>147</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
       "      <td>97919</td>\n",
       "      <td>3971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>196.860259</td>\n",
       "      <td>235</td>\n",
       "      <td>464</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>172.779785</td>\n",
       "      <td>3319</td>\n",
       "      <td>898</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 20 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 375
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:08:03.174535Z",
     "start_time": "2024-09-19T12:08:03.026314Z"
    }
   },
   "cell_type": "code",
   "source": [
    "user_id_union = list(set(all_data['user_id']))\n",
    "\n",
    "\n",
    "def action_type_select(data, num, new_name):\n",
    "    d = user_log.groupby(['user_id', 'action_type']).apply(lambda x: Counter(x))\n",
    "    e = dict(d)\n",
    "    k = []\n",
    "    for i in user_id_union:\n",
    "        try:\n",
    "            k.append(e[i][num])\n",
    "        except KeyError:\n",
    "            k.append(0)\n",
    "\n",
    "    data3 = pd.DataFrame({'user_id': user_id_union, new_name: k})\n",
    "    data_union_2 = data.merge(data3, on=['user_id'], how='inner')\n",
    "    return data_union_2"
   ],
   "id": "c130b6b8f585a241",
   "outputs": [],
   "execution_count": 376
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:11:58.825796Z",
     "start_time": "2024-09-19T12:08:03.176053Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#点击次数\n",
    "all_data = action_type_select(all_data, 0, 'action_type_sum_0')\n",
    "#加入购物车次数\n",
    "all_data = action_type_select(all_data, 1, 'action_type_sum_1')\n",
    "#购买次数\n",
    "all_data = action_type_select(all_data, 2, 'action_type_sum_2')\n",
    "#收藏次数\n",
    "all_data = action_type_select(all_data, 3, 'action_type_sum_3')"
   ],
   "id": "a2ce7892352cb84c",
   "outputs": [],
   "execution_count": 377
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:11:58.842732Z",
     "start_time": "2024-09-19T12:11:58.826801Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.head()",
   "id": "84a8548a16552344",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  label  target  age_range  gender  sell_sum  \\\n",
       "0    34176         3906    0.0       1        6.0     0.0       451   \n",
       "1    34176          121    0.0       1        6.0     0.0       451   \n",
       "2    34176         4356    1.0       1        6.0     0.0       451   \n",
       "3    34176         2217    0.0       1        6.0     0.0       451   \n",
       "4   230784         4818    0.0       1        0.0     0.0        54   \n",
       "\n",
       "   seller_unique  cat_id_unique  time_stamp_unique  ...  brand_id_most  \\\n",
       "0            109             45                 47  ...         4094.0   \n",
       "1            109             45                 47  ...         4094.0   \n",
       "2            109             45                 47  ...         4094.0   \n",
       "3            109             45                 47  ...         4094.0   \n",
       "4             20             17                 16  ...         1236.0   \n",
       "\n",
       "   action_type_most  seller_id_cnt  cat_id_cnt  brand_id_cnt  action_type_cnt  \\\n",
       "0                 0             70          98            70              410   \n",
       "1                 0             70          98            70              410   \n",
       "2                 0             70          98            70              410   \n",
       "3                 0             70          98            70              410   \n",
       "4                 0             10           9            10               47   \n",
       "\n",
       "   action_type_sum_0  action_type_sum_1  action_type_sum_2  action_type_sum_3  \n",
       "0                  0                  0                  0                  0  \n",
       "1                  0                  0                  0                  0  \n",
       "2                  0                  0                  0                  0  \n",
       "3                  0                  0                  0                  0  \n",
       "4                  0                  0                  0                  0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>target</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
       "      <th>sell_sum</th>\n",
       "      <th>seller_unique</th>\n",
       "      <th>cat_id_unique</th>\n",
       "      <th>time_stamp_unique</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_id_most</th>\n",
       "      <th>action_type_most</th>\n",
       "      <th>seller_id_cnt</th>\n",
       "      <th>cat_id_cnt</th>\n",
       "      <th>brand_id_cnt</th>\n",
       "      <th>action_type_cnt</th>\n",
       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2</th>\n",
       "      <th>action_type_sum_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34176</td>\n",
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       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
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       "      <td>0</td>\n",
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       "      <td>45</td>\n",
       "      <td>47</td>\n",
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       "      <td>0</td>\n",
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       "      <td>70</td>\n",
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       "      <td>47</td>\n",
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       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34176</td>\n",
       "      <td>2217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>451</td>\n",
       "      <td>109</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 378
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:11:59.102745Z",
     "start_time": "2024-09-19T12:11:58.843743Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train = all_data[all_data['target'] == 1].reset_index(drop=True)\n",
    "test = all_data[all_data['target'] == 1].reset_index(drop=True)\n",
    "train.drop(['target'], axis=1, inplace=True)\n",
    "test.drop(['target'], axis=1, inplace=True)\n",
    "train.fillna(0, inplace=True)\n",
    "test.drop(['label'], axis=1, inplace=True)\n",
    "test.fillna(0, inplace=True)\n",
    "test.head()"
   ],
   "id": "5e00e27e8a97536f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   user_id  merchant_id  age_range  gender  sell_sum  seller_unique  \\\n",
       "0    34176         3906        6.0     0.0       451            109   \n",
       "1    34176          121        6.0     0.0       451            109   \n",
       "2    34176         4356        6.0     0.0       451            109   \n",
       "3    34176         2217        6.0     0.0       451            109   \n",
       "4   230784         4818        0.0     0.0        54             20   \n",
       "\n",
       "   cat_id_unique  time_stamp_unique  action_type_unique  time_stamp_std  ...  \\\n",
       "0             45                 47                   3      206.449449  ...   \n",
       "1             45                 47                   3      206.449449  ...   \n",
       "2             45                 47                   3      206.449449  ...   \n",
       "3             45                 47                   3      206.449449  ...   \n",
       "4             17                 16                   2      218.341897  ...   \n",
       "\n",
       "   brand_id_most  action_type_most  seller_id_cnt  cat_id_cnt  brand_id_cnt  \\\n",
       "0         4094.0                 0             70          98            70   \n",
       "1         4094.0                 0             70          98            70   \n",
       "2         4094.0                 0             70          98            70   \n",
       "3         4094.0                 0             70          98            70   \n",
       "4         1236.0                 0             10           9            10   \n",
       "\n",
       "   action_type_cnt  action_type_sum_0  action_type_sum_1  action_type_sum_2  \\\n",
       "0              410                  0                  0                  0   \n",
       "1              410                  0                  0                  0   \n",
       "2              410                  0                  0                  0   \n",
       "3              410                  0                  0                  0   \n",
       "4               47                  0                  0                  0   \n",
       "\n",
       "   action_type_sum_3  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ],
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>action_type_sum_0</th>\n",
       "      <th>action_type_sum_1</th>\n",
       "      <th>action_type_sum_2</th>\n",
       "      <th>action_type_sum_3</th>\n",
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       "  </thead>\n",
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       "      <td>206.449449</td>\n",
       "      <td>...</td>\n",
       "      <td>4094.0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230784</td>\n",
       "      <td>4818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>218.341897</td>\n",
       "      <td>...</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 379
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:11:59.111587Z",
     "start_time": "2024-09-19T12:11:59.107265Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# import xgboost as xgb\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.metrics import roc_auc_score, roc_curve"
   ],
   "id": "5dc8b0b7209be54a",
   "outputs": [],
   "execution_count": 380
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:09.562223Z",
     "start_time": "2024-09-19T12:11:59.113596Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train.to_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\train_all_k.csv', index=False, header=True)\n",
    "test.to_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\test_all_k.csv', index=False, header=True)\n",
    "\n",
    "all_data.to_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\all_k.csv', index=False, header=True)"
   ],
   "id": "b206f57220073c42",
   "outputs": [],
   "execution_count": 381
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:09.574711Z",
     "start_time": "2024-09-19T12:12:09.563228Z"
    }
   },
   "cell_type": "code",
   "source": "train['label'].value_counts()",
   "id": "5ee6e9bf05febbb9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0.0    498929\n",
       "1.0     15838\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 382
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:09.580743Z",
     "start_time": "2024-09-19T12:12:09.575737Z"
    }
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "id": "f50ac1a3fef0cb33",
   "outputs": [],
   "execution_count": 383
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.098462Z",
     "start_time": "2024-09-19T12:12:09.581750Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_data = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\train_all_k.csv')\n",
    "test_data = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\test_all_k.csv')"
   ],
   "id": "6d71da5507f3c3db",
   "outputs": [],
   "execution_count": 384
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.105963Z",
     "start_time": "2024-09-19T12:12:11.099469Z"
    }
   },
   "cell_type": "code",
   "source": "train_data.shape",
   "id": "90e20a15ed31dc69",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(514767, 23)"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 385
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.118908Z",
     "start_time": "2024-09-19T12:12:11.107527Z"
    }
   },
   "cell_type": "code",
   "source": "test_data.shape",
   "id": "ffe9c203997e9ba",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(514767, 22)"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 386
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.919454Z",
     "start_time": "2024-09-19T12:12:11.123927Z"
    }
   },
   "cell_type": "code",
   "source": "all_data = pd.read_csv('C:\\\\Users\\\\20937\\\\Desktop\\\\阿里比赛\\\\data_format1\\\\all_k.csv')",
   "id": "e750162db87a5b54",
   "outputs": [],
   "execution_count": 387
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.926053Z",
     "start_time": "2024-09-19T12:12:11.920468Z"
    }
   },
   "cell_type": "code",
   "source": "all_data.shape",
   "id": "6292f3ef315101d2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(514767, 24)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 388
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.939008Z",
     "start_time": "2024-09-19T12:12:11.927195Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from functools import partial\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "#from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "import lightgbm\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score, consensus_score, log_loss, auc, \\\n",
    "    precision_recall_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "id": "eb1c9ed0f0465a28",
   "outputs": [],
   "execution_count": 389
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:11.946061Z",
     "start_time": "2024-09-19T12:12:11.941017Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def model_clf(model):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_train_pred = model.predict_proba(X_train)\n",
    "    y_train_pred_pos = y_train_pred[:, 1]\n",
    "\n",
    "    y_test_pred = model.predict_proba(X_test)\n",
    "    y_test_pred_pos = y_test_pred[:, 1]\n",
    "\n",
    "    auc_train = roc_auc_score(y_train, y_train_pred_pos)  #AUC评分\n",
    "    auc_test = roc_auc_score(y_test, y_test_pred_pos)  #AUC评分\n",
    "\n",
    "    print('auc_train:', auc_train)\n",
    "    print('auc_test:', auc_test)\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_test_pred_pos)  #计算真正率和假正率  roc曲线\n",
    "    return fpr, tpr"
   ],
   "id": "78ef5c3ff83f8a68",
   "outputs": [],
   "execution_count": 390
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:12.046730Z",
     "start_time": "2024-09-19T12:12:11.947065Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_data_1 = train_data\n",
    "train_data_1"
   ],
   "id": "68eddbe76cda3d4c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        user_id  merchant_id  label  age_range  gender  sell_sum  \\\n",
       "0         34176         3906    0.0        6.0     0.0       451   \n",
       "1         34176          121    0.0        6.0     0.0       451   \n",
       "2         34176         4356    1.0        6.0     0.0       451   \n",
       "3         34176         2217    0.0        6.0     0.0       451   \n",
       "4        230784         4818    0.0        0.0     0.0        54   \n",
       "...         ...          ...    ...        ...     ...       ...   \n",
       "514762   228479         3111    0.0        6.0     0.0      2004   \n",
       "514763    97919         2341    0.0        8.0     1.0        55   \n",
       "514764    97919         3971    0.0        8.0     1.0        55   \n",
       "514765    32639         3536    0.0        0.0     0.0        72   \n",
       "514766    32639         3319    0.0        0.0     0.0        72   \n",
       "\n",
       "        seller_unique  cat_id_unique  time_stamp_unique  action_type_unique  \\\n",
       "0                 109             45                 47                   3   \n",
       "1                 109             45                 47                   3   \n",
       "2                 109             45                 47                   3   \n",
       "3                 109             45                 47                   3   \n",
       "4                  20             17                 16                   2   \n",
       "...               ...            ...                ...                 ...   \n",
       "514762            278             71                110                   3   \n",
       "514763             17             14                  8                   3   \n",
       "514764             17             14                  8                   3   \n",
       "514765             33             24                 15                   4   \n",
       "514766             33             24                 15                   4   \n",
       "\n",
       "        ...  brand_id_most  action_type_most  seller_id_cnt  cat_id_cnt  \\\n",
       "0       ...         4094.0                 0             70          98   \n",
       "1       ...         4094.0                 0             70          98   \n",
       "2       ...         4094.0                 0             70          98   \n",
       "3       ...         4094.0                 0             70          98   \n",
       "4       ...         1236.0                 0             10           9   \n",
       "...     ...            ...               ...            ...         ...   \n",
       "514762  ...         7704.0                 0            147         284   \n",
       "514763  ...         2020.0                 0             18          20   \n",
       "514764  ...         2020.0                 0             18          20   \n",
       "514765  ...         5508.0                 0             11          12   \n",
       "514766  ...         5508.0                 0             11          12   \n",
       "\n",
       "        brand_id_cnt  action_type_cnt  action_type_sum_0  action_type_sum_1  \\\n",
       "0                 70              410                  0                  0   \n",
       "1                 70              410                  0                  0   \n",
       "2                 70              410                  0                  0   \n",
       "3                 70              410                  0                  0   \n",
       "4                 10               47                  0                  0   \n",
       "...              ...              ...                ...                ...   \n",
       "514762           147             1770                  0                  0   \n",
       "514763            18               46                  0                  0   \n",
       "514764            18               46                  0                  0   \n",
       "514765            12               62                  0                  0   \n",
       "514766            12               62                  0                  0   \n",
       "\n",
       "        action_type_sum_2  action_type_sum_3  \n",
       "0                       0                  0  \n",
       "1                       0                  0  \n",
       "2                       0                  0  \n",
       "3                       0                  0  \n",
       "4                       0                  0  \n",
       "...                   ...                ...  \n",
       "514762                  0                  0  \n",
       "514763                  0                  0  \n",
       "514764                  0                  0  \n",
       "514765                  0                  0  \n",
       "514766                  0                  0  \n",
       "\n",
       "[514767 rows x 23 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>label</th>\n",
       "      <th>age_range</th>\n",
       "      <th>gender</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>514763</th>\n",
       "      <td>97919</td>\n",
       "      <td>2341</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>514764</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514765</th>\n",
       "      <td>32639</td>\n",
       "      <td>3536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514766</th>\n",
       "      <td>32639</td>\n",
       "      <td>3319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514767 rows × 23 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 391
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:12.073346Z",
     "start_time": "2024-09-19T12:12:12.047736Z"
    }
   },
   "cell_type": "code",
   "source": "train_data_1 = train_data_1.fillna(0)",
   "id": "b54eddc398ac6654",
   "outputs": [],
   "execution_count": 392
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:12.078263Z",
     "start_time": "2024-09-19T12:12:12.073346Z"
    }
   },
   "cell_type": "code",
   "source": "label = train_data_1['label']",
   "id": "b6f302bc19e0b5a1",
   "outputs": [],
   "execution_count": 393
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:12.084779Z",
     "start_time": "2024-09-19T12:12:12.079269Z"
    }
   },
   "cell_type": "code",
   "source": [
    "del train_data_1['user_id']\n",
    "del train_data_1['merchant_id']\n",
    "del train_data_1['label']\n"
   ],
   "id": "64fe09f23963903d",
   "outputs": [],
   "execution_count": 394
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:13.995045Z",
     "start_time": "2024-09-19T12:12:12.085787Z"
    }
   },
   "cell_type": "code",
   "source": [
    "stdScaler = StandardScaler()\n",
    "X = stdScaler.fit_transform(train_data_1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label, test_size=0.2, random_state=9)\n",
    "# Split the data into training and test sets\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial', class_weight='balanced')\n",
    "model_clf(clf)"
   ],
   "id": "415fbf5e5c2c2299",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc_train: 0.5734875081099311\n",
      "auc_test: 0.5752639632467217\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.00000000e+00, 1.00200401e-05, 5.01002004e-05, ...,\n",
       "        9.99969940e-01, 9.99989980e-01, 1.00000000e+00]),\n",
       " array([0., 0., 0., ..., 1., 1., 1.]))"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 395
  },
  {
   "metadata": {
    "jupyter": {
     "is_executing": true
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# 定义一个函数，用于训练模型并进行参数调优\n",
    "def model_clf(clf, params):\n",
    "    # 创建网格搜索对象\n",
    "    grid_search = GridSearchCV(clf, params, cv=5, scoring='accuracy')\n",
    "\n",
    "    # 将网格搜索应用于数据\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    # 打印最佳参数和最佳得分\n",
    "    print(\"最佳参数:\", grid_search.best_params_)\n",
    "    print(\"最佳得分:\", grid_search.best_score_)\n",
    "\n",
    "    # 使用最佳模型对测试数据进行预测\n",
    "    y_pred = grid_search.predict(X_test)\n",
    "\n",
    "    # 计算模型的准确率\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(\"准确率:\", accuracy)\n",
    "    auc_test = roc_auc_score(y_test, y_pred)\n",
    "    print(\"auc:\", auc_test)\n",
    "\n",
    "\n",
    "# # 加载数据\n",
    "# train_data_1 = train_data_1.fillna(0)\n",
    "# label = train_data_1['label']\n",
    "\n",
    "# 数据标准化\n",
    "stdScaler = StandardScaler()\n",
    "X = stdScaler.fit_transform(train_data_1)\n",
    "\n",
    "# 将数据划分为训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label, test_size=0.2, random_state=9)\n",
    "\n",
    "# 创建逻辑回归模型\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial', class_weight='balanced')\n",
    "\n",
    "# 定义参数网格\n",
    "params = {\n",
    "    'C': [3.2],\n",
    "    'max_iter': [100],\n",
    "    'penalty': ['l2','l1']\n",
    "    \n",
    "}\n",
    "\n",
    "# 使用参数调优训练模型\n",
    "model_clf(clf, params)"
   ],
   "id": "5f03d4917c4830b2",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-19T12:12:14.079150Z",
     "start_time": "2024-09-19T12:12:14.078151Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# # X_train, X_test, y_train, y_test = train_test_split(train_data_1, label, test_size=0.4, random_state=0)\n",
    "# # X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)\n",
    "# # clf = xgb\n",
    "# # train_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)\n",
    "# # test_matrix = clf.DMatrix(X_test, label=y_test, missing=-1)\n",
    "# # z = clf.DMatrix(X_valid, label=y_valid, missing=-1)\n",
    "# X_train, X_test, y_train, y_test = train_test_split(train_data_1, label, test_size=0.4, random_state=0)\n",
    "# X_test_temp, X_valid, y_test_temp, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)\n",
    "# # 确保 X_test 和 y_test 是对应的\n",
    "# X_test = X_test_temp\n",
    "# y_test = y_test_temp\n",
    "# \n",
    "# clf = xgb\n",
    "# train_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)\n",
    "# test_matrix = clf.DMatrix(X_test, label=y_test, missing=-1)\n",
    "# z = clf.DMatrix(X_valid, label=y_valid, missing=-1)\n",
    "# \n",
    "# params = {'booster': 'gbtree',\n",
    "#           'objective': 'multi:softprob',\n",
    "#           'eval_metric': 'mlogloss',\n",
    "#           'gamma': 1,\n",
    "#           'min_child_weight': 1.5,\n",
    "#           'max_depth': 5,\n",
    "#           'lambda': 10,\n",
    "#           'subsample': 0.7,\n",
    "#           'colsample_bytree': 0.7,\n",
    "#           'colsample_bylevel': 0.7,\n",
    "#           'eta': 0.03,\n",
    "#           'tree_method': 'exact',\n",
    "#           'seed': 2017,\n",
    "#           'num_class': 2\n",
    "#           }\n",
    "# score_dict = {}\n",
    "# num_round = 10000\n",
    "# early_stopping_rounds = 100\n",
    "# watchlist = [(train_matrix, 'train'),\n",
    "#              (test_matrix, 'eval')]\n",
    "# model = clf.train(params,\n",
    "#                   train_matrix,\n",
    "#                   num_boost_round=num_round,  #训练的轮数，数生成的数量\n",
    "#                   evals=watchlist,  #训练时用于评估模型的数据集合，用户可以通过验证集评估模型好坏\n",
    "#                   early_stopping_rounds=early_stopping_rounds,  #早停法，当某轮训练后，验证集得分没有提升，则提前停止训练\n",
    "#                   #如果发生了早停止，模型提供额外三个字段 bst.best_score, bst.best_iteration, bst.best_ntree_limit\n",
    "#                   evals_result=score_dict\n",
    "#                   )\n",
    "# y_train_pred = model.predict(train_matrix)  #ntree_limit 用于预测数的数量\n",
    "# y_train_pred_pos = y_train_pred[:, 1]\n",
    "# \n",
    "# y_test_pred = model.predict(z)\n",
    "# y_test_pred_pos = y_test_pred[:, 1]\n",
    "# \n",
    "# print(\"y_test shape:\", y_test.shape)\n",
    "# print(\"y_test_pred_pos shape:\", y_test_pred_pos.shape)\n",
    "# \n",
    "# auc_train_roc_auc_score = roc_auc_score(y_train, y_train_pred_pos)  #训练集的auc\n",
    "# auc_test_roc_auc_score = roc_auc_score(y_test, y_test_pred_pos)  #测试集的auc\n",
    "# print('训练集的auc为：', auc_train_roc_auc_score)  #测试集的auc为： 0.8378378378378378\n",
    "# print('测试集的auc为：', auc_test_roc_auc_score)  #训练集的auc为： 0.8378378378378378\n",
    "# \n",
    "# fpr, tpr, _ = roc_curve(y_test, y_test_pred_pos)  #绘制roc曲线"
   ],
   "id": "2638332d8611a5c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# features_colums = [col for col in train_data_1.columns if col not in ['id', 'label']]\n",
    "# train_data_1 = train_data[features_colums]\n",
    "# test_data_1 = test_data[features_colums]\n",
    "# target = train_data['label']"
   ],
   "id": "5dd2501cb56d0163",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# def model(train_1, target_1):\n",
    "#     X_train, X_val, y_train, y_val = train_test_split(train_1, target_1, test_size=0.2, random_state=42)\n",
    "# \n",
    "#     clf = XGBClassifier()\n",
    "# \n",
    "#     clf.fit(X_train, y_train)\n",
    "# \n",
    "#     y_train_pred = clf.predict_proba(X_train)\n",
    "#     y_train_pred_pos = y_train_pred[:, 1]\n",
    "# \n",
    "#     y_val_pred = clf.predict_proba(X_val)\n",
    "#     y_val_pred_pos = y_val_pred[:, 1]\n",
    "# \n",
    "#     auc_train_roc_auc_score = roc_auc_score(y_train, y_train_pred_pos)  #训练集的auc\n",
    "#     auc_val_roc_auc_score = roc_auc_score(y_val, y_val_pred_pos)  #测试集的auc\n",
    "# \n",
    "#     print('训练集的auc为：', auc_train_roc_auc_score)  #测试集的auc为： 0.8378378378378378\n",
    "#     print('测试集的auc为：', auc_val_roc_auc_score)  #训练集的auc为： 0.8378378378378378\n",
    "# \n",
    "#     fpr, tpr, _ = roc_curve(y_val, y_val_pred_pos)  #绘制roc曲线\n",
    "#     return fpr, tpr, clf, auc_val_roc_auc_score"
   ],
   "id": "db3e16643881cddc",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# fpr_1, tpr_1, clf_1, auc_test_1 = model(train_data_1, target)  #06153997",
   "id": "3c1836ecc11d4930",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# def feature_selection(train, train_sel, target):\n",
    "#     clf = XGBClassifier()\n",
    "# \n",
    "#     scores = cross_val_score(clf, train, target, cv=5)\n",
    "# \n",
    "#     scores_sel = cross_val_score(clf, train_sel, target, cv=5)\n",
    "# \n",
    "#     print('No Select Accuracies: %0.2f (+/- %0.2f)' % (scores.mean(), scores.std() * 2))\n",
    "#     print('Features Select Accuracies: %0.2f (+/- %0.2f)' % (scores_sel.mean(), scores_sel.std() * 2))\n",
    "#     return scores.mean(), scores_sel.mean()"
   ],
   "id": "5439d94f83e9cbde",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# def select(train, goal):\n",
    "#     a_score = []\n",
    "#     b_score = []\n",
    "#     for i in range(2, train.shape[1] + 1):\n",
    "#         sel = SelectKBest(mutual_info_classif, k=i)\n",
    "#         sel = sel.fit(train, goal)\n",
    "#         train_sel = sel.transform(train)\n",
    "# \n",
    "#         print('训练数据末特征筛选维度', train.shape)\n",
    "#         print('训练数据特征筛选维度后', train_sel.shape)\n",
    "#         mean_train, mean_test = feature_selection(train, train_sel, goal)\n",
    "#         a_score.append(mean_train)\n",
    "#         b_score.append(mean_test)\n",
    "#     x = list(range(2, train.shape[1] + 1))\n",
    "#     plt.plot(x, a_score, marker='o', marksize=3)  #绘制折线图，添加数据点，设置点大小\n",
    "#     plt.plot(x, b_score, marker='o', marksize=3)  #绘制折线图，添加数据点，设置点大小\n",
    "#     plt.xlabel(x)  #x轴标签\n",
    "#     plt.show()\n",
    "#     return a_score, b_score"
   ],
   "id": "7c7d2fd937b4f46d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# a_score, b_score = select(train_data, target)",
   "id": "ae00e7a5c5636228",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# def select_fin(i, train_1, target_1):\n",
    "#     sel = SelectKBest(mutual_info_classif, k=i)\n",
    "#     sel = sel.fit(train_1, target_1)\n",
    "#     train_sel = sel.transform(train_1)\n",
    "#     test_sel = sel.transform(test_data_1)\n",
    "#     return train_sel, test_sel"
   ],
   "id": "8f84d74590dffc4b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# train_sel, test_sel = select_fin(4, train_data_1, target)",
   "id": "9ad0d781c13c3cd9",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "# fpr_5, tpr_5, clf_5, auc_test_5 = model(train_sel, target)",
   "id": "1c954a8da2b3df22",
   "outputs": [],
   "execution_count": null
  }
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